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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The tutorial as it appears at https://www.kaggle.com/c/datasciencebowl/details/tutorial"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Tutorial\n",
      "\n",
      "written by Aaron Sander, Data Scientist, Booz Allen Hamilton\n",
      "\n",
      "In this tutorial, we will go step-by-step through a simple model to distinguish different types of plankton and demonstrate some tools for exploring the image dataset. We will start by going through an example of one image to show how you could choose to develop a metric based on the shape of the object within the image. First, we import the necessary modules from scikit-image, matplotlib, scikit-learn, and numpy. If you don't currently have python installed, you can get the Anaconda distribution that includes all of the referenced packages below."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Import libraries for doing image analysis\n",
      "from skimage.io import imread\n",
      "from skimage.transform import resize\n",
      "from sklearn.ensemble import RandomForestClassifier as RF\n",
      "import glob\n",
      "import os\n",
      "from sklearn import cross_validation\n",
      "from sklearn.cross_validation import StratifiedKFold as KFold\n",
      "from sklearn.metrics import classification_report\n",
      "from matplotlib import pyplot as plt\n",
      "from matplotlib import colors\n",
      "from pylab import cm\n",
      "from skimage import segmentation\n",
      "from skimage.morphology import watershed\n",
      "from skimage import measure\n",
      "from skimage import morphology\n",
      "import numpy as np\n",
      "import pandas as pd\n",
      "from scipy import ndimage\n",
      "from skimage.feature import peak_local_max\n",
      "# make graphics inline\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import warnings\n",
      "warnings.filterwarnings(\"ignore\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Importing the Data\n",
      "\n",
      "The training data is organized in a series of subdirectories that contain examples for the each class of interest. We will store the list of directory names to aid in labelling the data classes for training and testing purposes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# get the classnames from the directory structure\n",
      "directory_names = list(set(glob.glob(os.path.join(\"competition_data\",\"train\", \"*\"))\\\n",
      " ).difference(set(glob.glob(os.path.join(\"competition_data\",\"train\",\"*.*\")))))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Example Image\n",
      "\n",
      "We will develop our feature on one image example and examine each step before calculating the feature across the distribution of classes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Example image\n",
      "# This example was chosen for because it has two noncontinguous pieces\n",
      "# that will make the segmentation example more illustrative\n",
      "example_file = glob.glob(os.path.join(directory_names[5],\"*.jpg\"))[9]\n",
      "print example_file\n",
      "im = imread(example_file, as_grey=True)\n",
      "plt.imshow(im, cmap=cm.gray)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "competition_data/train/acantharia_protist/101574.jpg\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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k8T4++6sjNy6TyXRMcCO+pijYN95lGQHA3XffjTfffNPZ7LBTpdtfTXmAbX9J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\nyERJa+UXUGz69cyeXBmpIbO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       "text": [
        "<matplotlib.figure.Figure at 0x1183bead0>"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Preparing the Images\n",
      "\n",
      "To create the features of interest, we will need to prepare the images by completing a few preprocessing procedures. We will step through some common image preprocessing actions: thresholding the images, segmenting the images, and extracting region properties. Using the region properties, we will create features based on the intrinsic properties of the classes, which we expect will allow us discriminate between them. Let's walk through the process of adding one such feature for the ratio of the width by length of the object of interest.\n",
      "\n",
      "First, we begin by thresholding the image on the the mean value. This will reduce some of the noise in the image. Then, we apply a three step segmentation process: first we dilate the image to connect neighboring pixels, then we calculate the labels for connected regions, and finally we apply the original threshold to the labels to label the original, undilated regions."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First we threshold the image by only taking values greater than the mean to reduce noise in the image\n",
      "# to use later as a mask\n",
      "f = plt.figure(figsize=(12,3))\n",
      "imthr = im.copy()\n",
      "imthr = np.where(im > np.mean(im),0.,1.0)\n",
      "sub1 = plt.subplot(1,4,1)\n",
      "plt.imshow(im, cmap=cm.gray)\n",
      "sub1.set_title(\"Original Image\")\n",
      "\n",
      "sub2 = plt.subplot(1,4,2)\n",
      "plt.imshow(imthr, cmap=cm.gray_r)\n",
      "sub2.set_title(\"Thresholded Image\")\n",
      "\n",
      "imdilated = morphology.dilation(imthr, np.ones((4,4)))\n",
      "sub3 = plt.subplot(1, 4, 3)\n",
      "plt.imshow(imdilated, cmap=cm.gray_r)\n",
      "sub3.set_title(\"Dilated Image\")\n",
      "\n",
      "labels = measure.label(imdilated)\n",
      "labels = imthr*labels\n",
      "labels = labels.astype(int)\n",
      "sub4 = plt.subplot(1, 4, 4)\n",
      "sub4.set_title(\"Labeled Image\")\n",
      "plt.imshow(labels)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.image.AxesImage at 0x1189f3510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Tp556aot6Z511Viy7ZdJK0s/tTN5DJ5F2WjYNyxLYuxthomrwmjG8nnivnmbc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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a4ccd90>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "With the image segmented into different parts, we would like to choose the largest non-background part to compute our metric. We would like to select the largest segment as the likely object of interest for classification purposes. We loop through the available regions and select the one with the largest area. There are many properties available within the regions that you can explore for creating new features. Look at the documentation for regionprops for inspiration."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# calculate common region properties for each region within the segmentation\n",
      "regions = measure.regionprops(labels)\n",
      "# find the largest nonzero region\n",
      "def getLargestRegion(props=regions, labelmap=labels, imagethres=imthr):\n",
      "    regionmaxprop = None\n",
      "    for regionprop in props:\n",
      "        # check to see if the region is at least 50% nonzero\n",
      "        if sum(imagethres[labelmap == regionprop.label])*1.0/regionprop.area < 0.50:\n",
      "            continue\n",
      "        if regionmaxprop is None:\n",
      "            regionmaxprop = regionprop\n",
      "        if regionmaxprop.filled_area < regionprop.filled_area:\n",
      "            regionmaxprop = regionprop\n",
      "    return regionmaxprop"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The results for our test image are shown below. The segmentation picked one region and we use that region to calculate our ratio metric."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "regionmax = getLargestRegion()\n",
      "plt.imshow(np.where(labels == regionmax.label,1.0,0.0))\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Sk+Z7RJlnhAzNJEhWNfAFedngNvTo0/SAYFH0KzwpaK+FNM1kEDTDLNDHKk0c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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1187d1910>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print regionmax.minor_axis_length/regionmax.major_axis_length"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.687475491704\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we collect the previous steps together in a function to make it easily repeatable."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getMinorMajorRatio(image):\n",
      "    image = image.copy()\n",
      "    # Create the thresholded image to eliminate some of the background\n",
      "    imagethr = np.where(image > np.mean(image),0.,1.0)\n",
      "\n",
      "    #Dilate the image\n",
      "    imdilated = morphology.dilation(imagethr, np.ones((4,4)))\n",
      "\n",
      "    # Create the label list\n",
      "    label_list = measure.label(imdilated)\n",
      "    label_list = imagethr*label_list\n",
      "    label_list = label_list.astype(int)\n",
      "    \n",
      "    region_list = measure.regionprops(label_list)\n",
      "    maxregion = getLargestRegion(region_list, label_list, imagethr)\n",
      "    \n",
      "    # guard against cases where the segmentation fails by providing zeros\n",
      "    ratio = 0.0\n",
      "    if ((not maxregion is None) and  (maxregion.major_axis_length != 0.0)):\n",
      "        ratio = 0.0 if maxregion is None else  maxregion.minor_axis_length*1.0 / maxregion.major_axis_length\n",
      "    return ratio"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Preparing Training Data\n",
      "\n",
      "With our code for the ratio of minor to major axis, let's add the raw pixel values to the list of features for our dataset. In order to use the pixel values in a model for our classifier, we need a fixed length feature vector, so we will rescale the images to be constant size and add the fixed number of pixels to the feature vector.\n",
      "\n",
      "To create the feature vectors, we will loop through each of the directories in our training data set and then loop over each image within that class. For each image, we will rescale it to 25 x 25 pixels and then add the rescaled pixel values to a feature vector, X. The last feature we include will be our width-to-length ratio. We will also create the class label in the vector y, which will have the true class label for each row of the feature vector, X."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Rescale the images and create the combined metrics and training labels\n",
      "\n",
      "#get the total training images\n",
      "numberofImages = 0\n",
      "for folder in directory_names:\n",
      "    for fileNameDir in os.walk(folder):   \n",
      "        for fileName in fileNameDir[2]:\n",
      "             # Only read in the images\n",
      "            if fileName[-4:] != \".jpg\":\n",
      "              continue\n",
      "            numberofImages += 1\n",
      "\n",
      "# We'll rescale the images to be 25x25\n",
      "maxPixel = 25\n",
      "imageSize = maxPixel * maxPixel\n",
      "num_rows = numberofImages # one row for each image in the training dataset\n",
      "num_features = imageSize + 1 # for our ratio\n",
      "\n",
      "# X is the feature vector with one row of features per image\n",
      "# consisting of the pixel values and our metric\n",
      "X = np.zeros((num_rows, num_features), dtype=float)\n",
      "# y is the numeric class label \n",
      "y = np.zeros((num_rows))\n",
      "\n",
      "files = []\n",
      "# Generate training data\n",
      "i = 0    \n",
      "label = 0\n",
      "# List of string of class names\n",
      "namesClasses = list()\n",
      "\n",
      "print \"Reading images\"\n",
      "# Navigate through the list of directories\n",
      "for folder in directory_names:\n",
      "    # Append the string class name for each class\n",
      "    currentClass = folder.split(os.pathsep)[-1]\n",
      "    namesClasses.append(currentClass)\n",
      "    for fileNameDir in os.walk(folder):   \n",
      "        for fileName in fileNameDir[2]:\n",
      "            # Only read in the images\n",
      "            if fileName[-4:] != \".jpg\":\n",
      "              continue\n",
      "            \n",
      "            # Read in the images and create the features\n",
      "            nameFileImage = \"{0}{1}{2}\".format(fileNameDir[0], os.sep, fileName)            \n",
      "            image = imread(nameFileImage, as_grey=True)\n",
      "            files.append(nameFileImage)\n",
      "            axisratio = getMinorMajorRatio(image)\n",
      "            image = resize(image, (maxPixel, maxPixel))\n",
      "            \n",
      "            # Store the rescaled image pixels and the axis ratio\n",
      "            X[i, 0:imageSize] = np.reshape(image, (1, imageSize))\n",
      "            X[i, imageSize] = axisratio\n",
      "            \n",
      "            # Store the classlabel\n",
      "            y[i] = label\n",
      "            i += 1\n",
      "            # report progress for each 5% done  \n",
      "            report = [int((j+1)*num_rows/20.) for j in range(20)]\n",
      "            if i in report: print np.ceil(i *100.0 / num_rows), \"% done\"\n",
      "    label += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Reading images\n",
        "5.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "10.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "15.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "20.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "25.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "30.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "35.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "40.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "45.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "50.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "55.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "60.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "65.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "70.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "75.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "80.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "85.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "90.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "95.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n",
        "100.0"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " % done\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Width-to-Length Ratio Class Separation\n",
      "\n",
      "Now that we have calculated the width-to-length ratio metric for all the images, we can look at the class separation to see how well our feature performs. We'll compare pairs of the classes' distributions by plotting each pair of classes. While this will not cover the whole space of hundreds of possible combinations, it will give us a feel for how similar or dissimilar different classes are in this feature, and the class distributions should be comparable across subplots."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Loop through the classes two at a time and compare their distributions of the Width/Length Ratio\n",
      "\n",
      "#Create a DataFrame object to make subsetting the data on the class \n",
      "df = pd.DataFrame({\"class\": y[:], \"ratio\": X[:, num_features-1]})\n",
      "\n",
      "f = plt.figure(figsize=(30, 20))\n",
      "#we suppress zeros and choose a few large classes to better highlight the distributions.\n",
      "df = df.loc[df[\"ratio\"] > 0]\n",
      "minimumSize = 20 \n",
      "counts = df[\"class\"].value_counts()\n",
      "largeclasses = [int(x) for x in list(counts.loc[counts > minimumSize].index)]\n",
      "# Loop through 40 of the classes \n",
      "for j in range(0,40,2):\n",
      "    subfig = plt.subplot(4, 5, j/2 +1)\n",
      "    # Plot the normalized histograms for two classes\n",
      "    classind1 = largeclasses[j]\n",
      "    classind2 = largeclasses[j+1]\n",
      "    n, bins,p = plt.hist(df.loc[df[\"class\"] == classind1][\"ratio\"].values,\\\n",
      "                         alpha=0.5, bins=[x*0.01 for x in range(100)], \\\n",
      "                         label=namesClasses[classind1].split(os.sep)[-1], normed=1)\n",
      "\n",
      "    n2, bins,p = plt.hist(df.loc[df[\"class\"] == (classind2)][\"ratio\"].values,\\\n",
      "                          alpha=0.5, bins=bins, label=namesClasses[classind2].split(os.sep)[-1],normed=1)\n",
      "    subfig.set_ylim([0.,10.])\n",
      "    plt.legend(loc='upper right')\n",
      "    plt.xlabel(\"Width/Length Ratio\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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erG7XvXt3UlNTWb9+PZmZmdy6dUtvWpocffv2Zf/+/Rw+fFh9j1alSpXU987o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\nRgbOzs60bt2ar776ihs3bnDo0CFeeuk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S9fPV3J6jF8Os\nx+Bm53X6+As6ffwFrc3OZdiq/nRa79Bfn/Dhx77d8/76XddwaWpWlYlJVSYmtTQ1O1CGrFj5uxkD\nzmkbrByzZcnpf3bvtCbjoMqSMW9W1jGKAXW2gfdmXB7/xsOt884sPkekn0kqE5N6d/K13Pbdz/6s\n9CsMqqwjxgAAAErPHw02ttZfz1h9Zk4LjzY+XBUxkg2IRW2+otnmlCShujA1u2HqRTWnXhy1ZHa+\nVZfqV39B+vD7Ja1fAJakXcu9j/z3f66XkayDjjoGAAAos1GNMk2fJ++ZmtJZoXZuc1pp3iGax3O2\ne56sfn+1+YqmX1mfZjCL/c12mKow630jG6UcMdaLQ4dsXEC67467im7CSKQL6MXMQkZJ+vjd9xXd\nBGTESp218t60kvPOAx/rvlGBdtTrrTu9tl28ONA+7rr3/oxbhSJRa3s36J2S/dqd1AcazZn1OZA/\nemvv+cVM9z2MMtWgdIRZHqPLrPzdtIA6G5ey5+x3pGw7o8qYXnA+NVMd6oJ2up9+98G1g3hQZ7vL\nc5Rpu+cZZqamXt6b6axQby6u5Do7VLvnyer3Z+Gau4WMw2DEWIGWpma1PN34ELdr37XcEQkAwBbS\naQsl6fLV/KZkra2utZ7nmj6nR+ymvlDR9HefkSRtu+2WbHcORG5Ud0rmuS5hP2uu+qO3ru8wimx1\nLdF8l7roP2e9z5GtgxrV3crpCDNGlwEoi35HyhbJX5/21uv2DL2fYfYBAMCoBTtiLIb5a9MPctOP\nHGl1kG1mZS5QC/P0Wsgo9T8XcXqxZtC76ZCfGOpsL6y8N8uUc9A7/NNpC89WlrVSb39xN4s1xlbq\n68+zOqKLyJ2kvyt/rR/WGIsLtTYevZwDZb3m6lK9e130n7PTNv3opQZlcbdy0Swcs1ZQZ+NiIecw\nGbMaBTYKo1rHKItRfNgadbY9/9jL8/046Pu+08+N6r1ZdL2K9Zq7P8vGk8eyuXZQpnWAs8SIsRFL\nD05JhS8QDpRBendZ2e+mAyzJe/7zMt7h749Gy3qU2DDS39Vln75P2lGO3xVgVVon/BqRTrsoSe9U\n5nR989+7tofbIQQACFdWo8BiEtIoPsTFP/byfD8O+r4vul4U/fyx8mfZWN1Rk7Rj6H3Gug5wsCPG\nQp2/tjZfaY0Sq58/33V7K3OBln2e8CxYyCgxT3hMQq2z/bLy3uwn56jmP8/DoGuM+aPRih4l1k2Z\n1vfB8Ki14UjrhF8j1hYWtfDoUS08elSHrn1f699JNd4b4KzUoBiOWTRQZ+NiIaeFjBLXDmJCnY2L\nlfemhWvu99x+sOgmlBojxgAAwEhdrNU1V63pilpdYwPuo6wjvLKwupa01gN6T60cHZN5jyIEAAAI\nVT/rR0qcVwFAHnYsLKjy1llJ0q5910Yzqqls0tnwLtu1U2vLjelpQ50VL9gRY8PMX5vOixnCixbr\nfKebMU94PEY1FzHyxzzhcSlTzgsrazpbWdaFIdae6TTCK4s1xgpXq+nM917Sme+9pKXKhUu+XcQa\nYyGPIiw7a7W2l/np/Xnx/fP12uqa5qo1zVVrqq32Xj/SjvR+f65f/dafvecXtX/6nPZPn9Ou5aWh\nf25DzhxHvh4/9mTb9SDc7LxOH39Bp4+/oLXZuUva6mbn267x4q+/cTGDmwGyWgOhTH83MRxrdTZ2\nZczZ7/qR3c6rypgxD1w7iAd1Ni5lf292WpusPjPXmqVtebr7OaCFa+5PvziR+T7T2fCqZ872NSte\nGZkcMZbOi3nloQNFN6WFtcfs8u8WK/viuAAwKL/W1Us+VWHR1hYWtfBE48Pl2ufvuOT7tdWkr7uS\ne5G+Pty1jLz1Mj+9Py++f76+Um90iEvS9fXe60jakd7vz+UtmZ3XwqONCyz7P9P7FESdfs7PeXk9\nkXZl2FhPtbbWdr2MC1OzevVPHpck3fLTn5f2Xr+hrRd++vMaP9+4L9Nf48Vff+OTteE7LmNdAwEA\nAACsTYbsBNsxFtv8tZ0uAFiY71SyMU+vn3FzZ9j4m42e9RgK+sfvvk+TM3TuxiC2OtuJhfojbcy5\nNDXbuoMqiykG/P11urnDv/B5qMuF6R31uvbPn2u0b3t/F0nvPPAxvTgV3x2L/u9k34cOZL6Ic/r6\nsCD56FFr49FL/WmMoJqS1Lm+7U7qjdFgfda/UfHP9XrJEyoLx6wV1Nm4WMhpIaPEtYOYUGfjYuW9\ned8dd7VuporVPbcf1GKzvwGXCrZjDAiZf4E4hs4wAGHJ+m76hXNTOvONJyRJ+++9fej21RcqWnj8\nZGN/fYyiiJn/O7m+ORJjWP2uhwFgeL2MEktHjYZQ/wYd9QYAABCyYdYKTD+HlekzmH+z097dNyjW\nLoN+ZmxL10aXpMs7TMeeTvW+1TYh8X8/Mawh1o3JNcZCYmG+U8nGPL0WMkrln4sYvbNSZ628N7PO\nuWENl8qF1npfKx1Gg+1YWBhoLZ1+RbHGWBcnJ05msp9+18NAPqi18bBQfyQ753oWjlkrqLNxsZDT\nQkbJzt8TCyzW2WHWYE4/h5XpM1h6s9PCo0f17F8+XnRzcpOujzX9yBEde+q7W26bro2+1XWOdKr3\nrbYpUr9rjPm/nxjWEOsmzu5fAACQK3/U2bbbbmk9vpaodcfUFbW6xpqP12fmthxV4N+NdU35zicL\nk/4+h/mdsJYl0J5/h6fFunNZvabTx1/Q2tVXSdp2yfdX1wZfz7Cf6SAbN068KUnatjtReS4RAaOT\n3qGdxRTXgCX+DAislQsA6EewI8aszF/LGmPxsJBRasxFjDhYqbNW3pujyllfW79j6kIfd8D5d2Ot\nrg1+hfrOAx8b+GfLKP19+r+TOw7e0dc+/LsZy3RXIhqotcXx7/Acpu6kQqs/y7MVjX/9MS1NzbX9\n/lI9aVs7ejnXW1tY1MKjR5VUu0+7kt44sfDoUa1dKM80LWU8ZjGYEOpseod2umbrIKwcsxZyWsgo\nZXPtwJ8Bod9RO8hOCHU2C1bem/1+3sxaY1rHxmwze88vDrSP9IaTysRkx2kA77n94JY/t+3ixYGe\nu0zaZcyb/zt8d/K19ZmGpgY/x8kLI8YCtTQ1q+XpWe4oAwAM5WKtrne4yzI6g64fxl23AAAA2es2\ngn+Y9YoAICb+Grb9rK+9upZovjkbxI6peU0986Ik6Ya7b2+7Dli79cFq8xXNjr8iaePMOD7rs050\n4/8Orzx0QIvNf2exvn3Wgh0xZmX+2k5rjKVTWA1zR1mZWJhD++HHvq1TM9XSLbCZNeYJj4eVOhtz\n/fHXAXv8Gw+33ebCytold1n664HtWFgY+Pl31Osjv9vKwho/vawxNuj6Ydx1O3qWa+3FWr11bnSx\nNprjLZ2edK5a02rGH2TLWn/8u26zWOMxz3M9//W5WL2Y6R2mG9bG7GF/MZ8fWGO5zsYo1JzdRvD7\n33/020cKaOHoce0gHtTZuGS1pvWoLXmzQSzV19r+218H7NjExEDrg2U960Se+l1jzBpGjAE58u/6\neudCTTNnKpKkW6/bU2SzABjhrwNWu2Fvzz/nrwe2PHZYp666StLGu1d7WROsvlDR9HefkdT5bisA\ndqUd85L0ydpopvlMpyeVpLGSf5DNin/Xbbs1HsvEf32uf2dB7778PUnZ3GHq/00s4x2rAAAAAEYn\n2BFjVuavZY2xsPl3fX3k0L1FN2ckBp0nPJ2+69RMVXPVlYxbhUFYqbOx1p/NBv174q8x448gympN\nsKyFtsbPIDrN+Z7FXOwYPWptPCzUH8nOerIWjlkrqLNxGXXOuepK63Pq6/MXRzILzF333p/bvssk\n678nvVxT4LpDPqizcRl0jTH/8+gwM8+Myt235Xfunk63OFetqbY62A2AWeyjiDXGetHvTA55YcRY\nyaUL1kliPTFEbXF5VZPn3pUk3X/TGHOqAxnwR3VdUatrbMj9+etP1TPoDFtdSzLdn3WDzsUOlIG/\nJsDlA37wQ1zSaRUlaefF2pbrYfrrZb5nRNNyAnng83/5pDe7So2ZXyabtYZZYMqnl2sKXHcA8uN/\nHq1f/QXpw+8vuEXFSadblKTr+5imMet9lFVZZnIIdsSYlflrnzx6VNOPHIlqPbF2LMzTa2X+bCs5\nLbBSZ2OuP/6oriPPtF+zsh/++lP9zMHdiT8aLYv9SeVd4ydLoc75jvaotQ3+mgBZ1YNRs1B/pNGd\n66XTKp6tLKuytPX6h/56mRf6WFdxKzGfH1gTUp2tzVcG/vxv5Zi1kPPEU8eKbsJIcO0gHiHV2WFY\nqD+Snc+bz7wc/7k7a4xtjRFjAErHH5XS7q5gAPnYsbCgyltnte3ixbbfb0yNMCVJ2rWdER2D8EdA\ndFqbreyo0cDo7U7q2j99TlJv9bff7cvOz7NjLJE2jdTYUa9r/3zj+9t2J2qXuNMo6qWp2VYHRD8j\ndPy1hKmFQHzS93ieUyZK6+dVeT8PgPxY/nzU6RqBPzvMMPUtPQfcu/sGlb0bo9Nnff/xLGbGSKdY\nzGp/lgU7YszK/LX+XKDptAqViUnVz1cLbFX2LMzTa2U9hixy+qNS2t0VjNGwUmct1B9Juuvj3ecJ\nr8/MafqRI1q70P5vTDo1wsKjR5VUy/l3qOxr/PgjIAZdm23QOd+zQo3OFrU2HnnWn7WFxb7qb7/b\n96OIc1o/T31m7pLv1xcqre93+hvmj6L2R5WlU7lsHqHT7Zj11xKmFpYbdTYuo8qZvserGY1C7SQ9\nr/KfhzXGEBrrdTa2z0f9fN7sdI3Anx1mmDqangMms/MD76OTrNcY6/RZ3388i5kxVvqYaaOsa4yV\nRbm7WrFBbb6i2fFXJElXHjpQcGvQiX/3KHd9ARiFYe5a7+Uu+0HFMDqq7IoexWf57kgldu7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tVKLxbpdDr1GtK0aVNeffVVNT6UtO69e/d4++238fPzw8/Pj3feeYfs7Gx1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\n5saNG3oV7qJp6N+/v3pjLCYmRr2xBuXrTDVo0CC6d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Ss8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\nfHg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vTdGPC6WNVqslICCAK1euYGdnuT+nfvxMTU0lKCiI5ORkq2Xl\nwixX3DFaFJ/iiLOlNSY8znJVYc6lx+XQoUO8+eabXL58meXLl7Ny5UpCQkLo378/a9asYdWqVWzb\ntu2JpEUIUXBSni1fHnc+S+s1syhM3fsWtC2iMPWxs2bNYsSIEYwYMaLIaS1JZaFRDIrUMPaQA8k3\niyMtAAR5VntiB7NOp3si2ykpDx8+pEKFQv/EgtyXRdrbF/8r9kriN5IGYSHKj/JyTZa4lKu494N+\n4fTQoUP8+OOPxMfHU7FiRYAiNYoJYStKU7xt3rw5S5YsoX379o+83cdx01oaGoz046eHh0eBO28V\nZjm5Vgl9pSkmGCvIsVqQOGJ8ThS2g2VRGFcEdu/evdi29bg8if0ihBBlWWm+ZhorznrawpQlU1NT\nadiwYbGkQ/ylTA+luGjRIpo0aYJGo+G5555j7969BsMj5g3t98UXX9CkSRMaN27M0qVL1eUVRSEz\nM5PRo0ej0WgICgoyGBf3f//7Hz179sTb25ugoCC+/fZbdVpERAQTJkwgLCwMjUZD165dOX/+vDr9\n559/pnPnznh5edGlSxd++eUXdVrPnj358MMP6dKlC56engwaNIgbN24Y5G39+vX4+/vj5+fH/Pnz\n1e8fPHjAu+++S5MmTWjSpAmTJk0iMzMTyG21btKkCYsXL6ZRo0aMHTsWnU7HwoULadWqFb6+vvz9\n73/Pty1TDh06xMsvv4y3tzfNmjVj7dq1AOzcuZMOHTrg6elJs2bNmD17tsnlY2Nj6dSpk8F3y5Yt\nY9CgQQDcu3ePKVOm0Lx5c7y8vOjevTv3798HYPv27QQGBuLt7U2vXr04c+aMuo7mzZuzcOFCAgMD\nqV+/Pm+++SYPHjzIt52GDRvSuHFj1qxZo35/69YtRo0aRYMGDWjevDnz5s1TG0fXrFlDt27dmDx5\nMr6+vsyePZvz58/Tu3dvfH198fPzY+TIkdy6dcsgLUuXLuWFF17Ay8uLYcOG5UuLMVO/UWZmptnf\nFGDbtm20b98eT09PWrVqxZ49e/KtNz09nXbt2qnH96+//qr+fu3bt+enn34CYPr06Rw8eJDo6Gg0\nGg0TJ04EYNKkSTRs2BBPT0/atWvH6dOn1XXfuHHD7HHu6uqqfo6IiCAyMpLg4GA0Gg09e/YkNTXV\n4v6YMGECU6dONfhu4MCBfPrpp4Dlc9B4KMk1a9YY3Di5urqycuVKWrdujbe3N1FRUeq0nJwcpkyZ\ngp+fHwEBAfzrX/8qs0OYFicZJ1w8KeW9o0pBPcn9kJKSgkajURvFRMmRWCsKS1EUiZtGiro/SvN+\nfPiw+HpW2xpbibN5CnJcW4sjpo6/J9E4XBYrAqXRXAjbibO2Up4tj/k0V59cmHrajIwM+vXrR3p6\nOhqNBo1GQ3p6ukFbxP379xk5ciS+vr54e3vTpUsXrly5YrY+1pSWLVty/vx5Bg4ciEajITMzk+bN\nm/Pjjz+q8+hvMyoqSk2PRqOhVq1azJ49m82bNxt87+bmRq9evTh69CjPPPOMQTlg69atVjvdWWqT\nMLW/9+7dy8WLF3F3dzdoizh+/Dh+fn5kZ2cDsGrVKtq2bUv9+vXp27ev1brkx6nMNoydPXuWf//7\n3+zZswetVsvGjRvRaDQmCyU//fQThw8fJiYmhsWLF6sHkk6n49tvvyU4OJjk5GReeeUVtfI8KyuL\ngQMH0rlzZ86ePcvs2bMZMWIE586dU9e7efNmoqOjSUpKon79+kyfPh2A69evExYWRnh4OImJiYwa\nNYqwsDCDg+Drr79m6dKlnDp1Cnt7+3wnxM8//8yvv/5KbGwsH3/8MWfPngVg3rx5xMXFsXfvXvbu\n3UtcXBxz585Vl7ty5Qo3btzg+PHjzJ8/n88++4zt27fzzTffcOrUKVxcXJgwYYLFfZuSkkK/fv0Y\nOXIk586dY+/evTRr1gyAypUrs3z5cpKTk/n6669ZsWKFyaEMXnnlFZKTkw0atdavX09YWBgA7733\nHr///js7duwgMTGRDz74ADs7O86dO8eIESOYNWsW586do0uXLgwcONCgYB4TE8PGjRuJi4sjISHB\nIP+XL1/m9u3bxMfHs2jRIqKiotTGrOjoaO7cucPRo0f55ptv+Prrr1m9erW6bFxcHN7e3pw5c4bI\nyEh0Oh2RkZGcOnWKQ4cOceHCBWbNmqXOrygK//nPf4iJieHYsWOcPHlSbUC0xPg3mjt3rtnf9MiR\nI4wePZpp06aRnJzMN998Q7169QzWl5ycTK9evRgxYgRvvvkmaWlpDBgwgAkTJpCUlMSHH37I66+/\nzrVr15gyZQqBgYHMmTMHrVbLrFmz+O677zh06BC//vorycnJrFixgurVq6vr37Rpk8nj3JSYmBii\noqI4d+4cTZs2tfrI74ABA9i0aZMajK9evcrevXsJDQ01ew4mJCSo+9/aTcjOnTv57rvv2LdvH7Gx\nsXz33XcAfPHFF3z33Xfs3buXH374gW3btskNjRCPQL+RHAzfT5hX0DTXaUHf7du36dWrF++++666\nnqJ0Qtm3b5/B8A99+vShS5cu6ufu3bur43QXpZODcUO88T6wlu5Tp07Rp08ffHx8eOaZZ1iwYIHJ\n7QwdOpRGjRrh5eVFjx49DDot7Nq1i8DAQDQaDU2aNFE7Rly9epWwsDC8vb3x8fHh1VdfVZfJK0x/\n9dVXvPXWW/z6669oNBpmz56db0zwixcvMmTIEBo0aEBAQACff/65Ou3evXtERERQv359AgMDiYuL\ns7i/hBCPT3h4OKmpqeqN8uLFi/O9YzfvRhRyb5rfeOMNsx0B9W+ys7OzmT9/Pq1atUKj0dCpUyfS\n0tIAmDhxIs2aNcPT05NOnTpx6NAhAHbv3s3ChQvVG+8OHToAuR3SxowZQ+PGjWnSpAkzZsyw2gEp\nJyeHqVOn4ufnR8uWLdm5c6fB9IsXLzJw4EB8fHx49tln+fLLL02ux/i9x0VdLjk5mR49eqDRaAgO\nDubatWsW06+/jnXr1pns5Gj8/l7j2Nu8eXMWL15Mu3bt0Gg0ZGdnm+wMChS5A6QoX44fP86LL76I\nRqPJV4bZsWMH7du3x9vbm27duhEfHw/kjyNLlixRj91Vq1bh7+9Pnz59SElJwdXVlezsbJMVeqbe\nMa7feTExMZEePXrg5eWFn58fw4YNs5gXUxWBlt6rbS4uQW7sGzp0KOHh4Wg0Gtq1a0dCQgILFiyg\nYcOG+Pv78/3336vzW4pZa9as4ZVXXuG9996jfv36BAQEsHv3bsB8x1NLHaUtVW6aq1AVQgjx6EzV\nJyuKUuB62sqVK7Nhwwbc3NzQarVotVrc3NwM6ifXrVvH7du3OXHiBImJicyfP5+KFSuarI81Jy4u\nDg8PD9auXYtWq8XR0TFfHaj+/3nr1Gq1/Pe//8XFxYVXX32VPn36qN+fPHkSLy8vQkJCCAgIoHr1\n6modKRjW2ZtjrU1CX1766tSpQ+vWrdmyZYvB79C7d2/s7e3Ztm0bCxcu5KuvvuLcuXMEBgYyfPhw\ni+l4nMpsw5i9vT2ZmZmcPn2arKwsPDw88PLyMtnrKSoqikqVKtG4cWMGDhzIxo0b1Wlt27alS5cu\nKIpCaGgoJ0+eBODw4cPcvXuXt956iwoVKvDCCy/w8ssvGyzbo0cPAgICsLe3p2/fvvz+++9AbmW8\nr68voaGh2NnZERISgp+fn1oJpygKYWFhPPPMMzg7OzNp0iRiY2MN0h4VFYWTk5PaCps3dunGjRuZ\nMGECrq6uuLq6EhUVxfr169Xl7OzsmDhxIg4ODlSsWJGVK1cyefJk6tSpg4ODA1FRUWzZssXijWlM\nTAwvvvgiwcHB2NvbU716dfWG7fnnn6dRo0YANG7cmD59+qhPI+lzcnLitddeY8OGDUBuJWBKSgov\nv/wyOTk5rFmzho8++gg3Nzfs7Oxo3bo1jo6ObN68mZdeeokOHTpgb2/PmDFjuHfvnlqQVBSF4cOH\nU7duXVxcXIiMjGTTpk3qdvPyaG9vT9euXalcuTJnz54lOzubzZs3M3XqVCpXrky9evUYPXq0wb5z\nc3Nj+PDh2NnZUbFiRby9venQoQMODg64uroyatSofMPNjBw5ktq1a+Pi4kK3bt3UY8AS49/I0m+6\natUqBg0apFYy1KlTBz8/P3Vdp0+fpnfv3kycOJEhQ4YAsGHDBrp27apWAr/44ou0aNHCoHJB/1hz\ndHTkzp07nDlzhpycHPz8/Khdu7Y63dxxbsrLL79M27ZtcXR0ZMqUKfz6669qhYopLVu2pGrVquqN\nwaZNm2jXrh1PP/202XMwJibG6j7OM27cOJ566ik8PDxo166den7HxsYSHh5OnTp1qFatGm+99Vap\n7jFcUmSccPEo9AtqV65cMdtpIW/ea9eu0adPH9q2bctHH32kTitKJ5Rnn32WxMRErl+/TlZWFvHx\n8aSnp5ORkcG9e/f47bffCAwMVLddlE4O1phL9+3btwkODqZr166cOnWKw4cPm+2Z9dJLL3H48GHO\nnj2Lv78/I0eOVKeNHTuWBQsWoNVqOXjwoLqOZcuW4e7uzrlz5zhz5ozBU7l5henBgwczb948Wrdu\njVarJTo62mC7OTk5DBw4EH9/f+Lj44mNjWX58uXqE8tz5swhOTmZo0ePEhMTw7p166RzwSOQWCsK\nY/ny5QY3yi1btsw3j/H5uGPHDpMdAfPmzZt/2bJlbNq0ifXr16PValm6dCmVKlUCoFWrVuzbt4+k\npCRCQkJ44403yMzMpEuXLowfP57g4GC0Wq1apouIiMDR0ZEjR47w448/8v3335ttkMrzxRdfsHPn\nTn788Uf27NnDli1bDPIyfPhwPDw8OHXqFCtXrmT69Ons27fP6j4r6nL/+Mc/CAgIICEhgQkTJrB2\n7doCxzpznRzB+tMleb9BUlISiYmJJjuDAkXqAGmrymuczczMZNCgQYSFhZGUlETv3r3ZunUriqJw\n/Phxxo4dy8KFC0lMTGTo0KEMHDiQrKysfHFkzJgx6joPHjzIzz//TExMjHp/pChKgSv09GPKzJkz\n6dy5M+fPn+fkyZNWO00WpCJQn7m4lGfnzp3079+fpKQk/P39CQ4OBiA+Pp533nmHyMhIdV5rMSsu\nLg4/Pz8SEhIYO3Ys48aNAzC5X6x1lDZVuWmtQlWI0q68xlljtlKeLY/5tFSfXJh6WlN1hzqdTv3e\nwcGBa9eukZiYiKIo+Pv7U7VqVYN5i8sff/zBoEGDmD17tkHHq5ycHP7xj3/wwgsv8PrrrwMQFham\n1tlfv36d77//nr59+1pcv7U2CXNCQkLUfa3T6di8ebO6rRUrVvDWW2/h5+eHnZ0d48eP58SJE0/s\nqbEy2zBWv359Zs6cyezZs2nYsCHDhw8nPT3d5Lzu7u7q/x4eHgbz1apVS/3f2dmZ+/fvk5OToz7q\np69evXoGy9asWVP9v1KlSmRkZAC5w9p5eHhYXNY4TVlZWVy9elX9Tr9hwtnZ2WDd+k8MGefH1dUV\nR8e/xlVNSUlh8ODBeHt74+3tTWBgIBUqVODy5cv5d9Sf0tLS8PLyMjnt8OHD9OrViwYNGuDl5cUX\nX3zB9evXTc4bFhamNmKsX7+ePn364ODgwNWrV7l//77JbVy6dMlg3ymKgru7OxcvXlS/s/R7Vq9e\n3eDl3Hm/y9WrV8nKysq378ytF3KfPhs2bBhNmjTB09OTUaNG5espqn/8VKxYUf2dLDH+jSz9pmlp\naXh7e5tcj06nIyYmhrp169KrVy/1+5SUFP7zn/+ov7m3tze//PKLwW+uXxB/4YUXGD58OFFRUTRs\n2JDx48dz+/Ztdbq549yUunXrqv9XrlyZ6tWrmz0v8+gH4/Xr19OvXz+AAp2D1uifR5UqVeLOnTtA\n7j7XX7d+uoUQj4d+gc9cp4U8Fy9epGfPnrz22mtMmjTJYD1F6YRSqVIlAgIC+Omnnzh27BhNmzbl\nueee49ChQxw+fJj69evj4uKibqMonRyssZRuNzc3Ro8ejaOjI1WqVKFVq1Ym1zFw4EAqV66Mg4MD\n0dHRnDhxQo3PDg4OnD59mlu3bvHUU0/h7++vfn/p0iW0Wi329va0bdvW5LotFcjj4uK4evUq77zz\nDhUqVMDT05PBgwerhdn//Oc/REZGUq1aNdzd3Rk5cqR0LhCiFDPXEdDYqlWrmDJlCj4+PkBuJ7i8\nUQRCQ0NxcXHBzs6OiIgIHjx4oI6koV8ZALll6N27dzNjxgwqVarE008/zahRo9i8ebPFdMbGxjJq\n1Ci1wmL8+PHqelNTU/nll1/45z//iaOjI02bNmXw4MGsW7fO4jofZbljx44xadIkHBwcCAwMpFu3\nbgWOdeY6OYLl+KsoCiNGjKBu3bo4OTmZ7QwKFKkDpChfDh8+THZ2NuHh4djb29OrVy8CAgLQ6XR8\n+eWXvP7667Rs2VLtmOvk5MThw4ctrjM6OppKlSrh5ORkcnphrveOjo5otVrS0tJwdHTkueeeK1T+\nrLEUlwACAwPp2LGjum+uX7/OW2+9hb29vdqL/tatWwWKWfXq1WPw4MEoikL//v1JT083eJJLf7+Y\nK6PqvxZAX2EqVIUQQhSdufrkwtTTWtO/f386deqk1ie///77BqOgFVeH0qysLIYOHUq/fv3o06eP\nwbTp06dz9+5dg04tffv25dtvv+Xu3bvExsYSGBhoUMdtSlH3S8+ePfn111+5dOkSBw4cwM7OTq2n\nSElJYdKkSWr9dd59iH59fXEqsw1jkNviuG3bNn777TcUReGDDz4weYDptzKmpqZSp04dq+uuU6cO\nFy5cMCjgpKSkFHjZlJQUg++MlzVOU95TSdbkPa6pv6ybm5v62Tj/Hh4ebNiwgaSkJPXvwoULBssY\nc3d3NxjySd+IESPo3r07J06c4Pz58wwdOtTszVfeU2AHDhxg48aNaoOHq6srFStWJCkpyWT+9Ped\nTqfjwoULBvvuwoULZvNvjqurKw4ODvn2nX6DiPG+mzZtGvb29hw4cIDk5GQ+/fRTizeaBQ1uxvOZ\n+k3z8uvu7k5iYqLZ9UycOJEaNWrwj3/8Q02bh4cH/fr1M/jNtVotY8eONZvOESNGsGfPHg4ePEhC\nQgJLliwpUF6M6f82d+7c4fr161Z/n9DQULZt28aJEyc4e/asOuyXtXPQ2dmZu3fvqtMsNfYac3Nz\nM0ir/v/iLzJOuHhczHVayLNr1y4ePHjA0KFD8y1b1E4oQUFB7N+/n4MHD/L888/z/PPPc+DAAQ4c\nOMDzzz9vsFxROjlYYy7dFy5cwNPT0+ry2dnZfPDBB7Rq1QpPT09atGihPlkHuU9W7N69mxYtWqgF\nTYAxY8bg7e1NSEgILVu2ZNGiRYVOe0pKCunp6QYdLBYsWMAff/wB5O9cYPw7iMKRWCuKm7mOgMYs\ndY5bsmQJbdu2xcvLC29vb27dumXQqU9fSkoKWVlZNGrUSI0hkZGRagwxx1JsSU9Pp3r16lSuXNlg\nurWb5qIud/HiRVxcXNQn5oB8w5lbYq6TY0Ho7wNLnUGL0gHSVpXXOHvx4sV89RN5x2lKSgqffPKJ\nwbU8LS3N6rFv3DHRWGEq9N5//310Oh1du3YlKCjI4DUGj4O1uKRfFqtYsSI1atRQ0593bmdkZBQo\nZhnH0bxl8+jvF3NlVHP7Xn9ZaxWqQpRW5TXOGrOV8mx5zae5+uSC1NOam9f4uwoVKhAVFcXBgwf5\n9ttv2bFjh9oh61EaxYzrQC9dumQwPTo6mmrVqjF58mSD7zdu3MjmzZtZuXIl9vb26vfu7u48++yz\nfPPNN6xfv57+/ftbTYO1NglzXFxc6NixI5s3byYmJoaQkBB1moeHBwsWLDCow05NTaV169ZW1/s4\nlNmGsbx3Xz148AAnJyecnJwMKt30zZs3j3v37nHq1CnWrl2br+XUlFatWlGpUiUWL15MVlYW+/fv\nV4chsaZLly4kJCSwceNGHj58yKZNmzh79iwvv/wykNvYs379ev73v/9x9+5dPvroI3r37l2gEyQ4\nOJh58+Zx9epVrl69yscff6w2OJkydOhQpk+frjbE/fHHH+qQjub07duXH374gdjYWB4+fMi1a9fU\nXo4ZGRm4uLiowwxs3LjRYrr79etHVFSUQQ8xOzs7/va3vzFlyhTS09PJzs7ml19+ITMzk9dee41d\nu3axd+9esrKyWLp0KRUrVqRNmzbqvvt//+//kZaWxvXr15k/f36BfhN7e3tee+01ZsyYwZ07d0hJ\nSeHTTz8lNDTU7DIZGRk4OztTtWpV0tLSrDYWFbW3vKnfNC9dgwYNYs2aNezdu5ecnBzS0tIMnrKo\nUKECK1as4O7du4waNQqdTkdoaCg7dugu5Q8AACAASURBVOxgz549ZGdnc//+ffbv368OaVizZk2D\nhs+jR49y+PBhsrKy1N6B+sGyMHbt2sWhQ4fIzMxk5syZtG7d2urTWO7u7rRo0YJRo0bRq1cvtXfi\ns88+a/EcbNasGd988w337t0jMTGRVatWWU1f3m/02muv8dlnn3Hx4kVu3rzJokWLZBgwIR6BqUJa\nYc6pwYMH06lTJ/r372+wHkusdUJ5/vnnDRrG8hrKTDWM6StIup2dnbl375762bhQaom7uzvJyclW\n54uJiWH79u3ExsaSnJzMsWPHDHoUBwQEsGrVKs6ePUv37t35+9//DkCVKlWYNm0acXFxrF69mk8+\n+aRAQ4YZp9HT0zNfB4u8An3t2rXzdfARQjw5+nHKOB5lZ2ebbbCyxt3d3WTHtYMHD7J06VJWrFjB\n+fPnSUpK4qmnnjIYZs14PU5OTiQkJKgxJDk52eTw6/qMOy7pxxY3NzeuX7+uPv2fN91aOfNRlrtx\n44bBNSklJeWRy4uVK1e2ev0w3oapzqBQtA6Qonxxc3PL19iSVzZyd3cnMjLS4PhISUlR76XMHcuW\njnHjaXkNROYq6mrVqsXChQs5efIk8+fPZ8KECWY74BaWtbhUGEWNWXmM94u1Mqqlyk1LFapCCCGK\nrjD1yZbq3mvWrMn169cNXg2hf+3Zv38/8fHxZGdnU6VKFRwcHNQ6VuP62MJo1qwZmzZt4uHDhxw9\nelQdOhlyRxE4cOAAy5cvN1jm+PHjTJw4ka+++ooaNWrkW2dYWBiLFi3i1KlT9OjRw2oaCtsmoS8k\nJIR169axdetWgyEb33jjDebPn6++T/3WrVvExsYWaJ2PQ5ltGMvMzOTDDz/Ez8+PRo0ace3aNd57\n7z0gf8EkKCiIZ599luDgYN58801efPFFdT7jefM+Ozo6smbNGnbv3o2fnx9RUVEsX74cX1/ffPMa\nf65RowZr165l2bJl+Pr6smzZMtauXasORZL3+H1ERASNGjUiKyvL4HFGS4XRd955hxYtWvDCCy/w\nwgsv0KJFC9555x2zy4aHh9OtWzdCQkLQaDS8/PLLxMXFmd+x5N5krV+/nmXLluHj40OHDh3UIVc+\n/vhjPvroIzQaDXPnzs3XyGi8/f79+3P69Ol8DVAffvghjRo1onPnzvj4+DBt2jRycnLw9fVl+fLl\nREdH4+fnx65du1izZg0VKlRQ19+3b1+1J3z9+vV5++23C7TvZs+ejbOzMy1btqR79+6Ehobyt7/9\nTV3OeNmoqCiOHz+Ol5cXAwcOpGfPnlZvFApys2w8j6XftGXLlixdupTJkyfj5eVFr1698lVAOjg4\n8OWXX3L58mXGjBlD3bp1WbVqFQsWLKBBgwb4+/uzbNkyNVCPHDmSLVu2UL9+fd59911u377N+PHj\n8fHxoUWLFri6uhqMM2/uODc1rW/fvsyZMwdfX19+//13PvvsM6v7A2DAgAHEx8cb9FBwcHCweA6O\nGjUKBwcHGjZsyJtvvkloaKjFtOl/N2TIEDp27MgLL7xAx44deemll7C3tzfbuG6rZJxwUVBNmzYl\nJiaG7Oxsdu/ezcGDBwu9jrzYMXDgQO7fv291fmudUNq0acO5c+c4evQorVq14plnniE1NZUjR44Q\nFBRkdr0FqVBp2rQpp0+f5sSJE9y/f5/Zs2cXOJ8vvfQSly5dYvny5Tx48IDbt29z5MiRfPNlZGTg\n5OSEi4sLGRkZTJs2TZ2WlZXFhg0buHXrFvb29lSpUkUtbO/YsYPExER0Oh1Vq1YtUmxr1aoVVapU\nYfHixdy7d4/s7Gzi4+M5evQokNu5YOHChdy8eZMLFy7wr3/9q1DrF4Yk1orCqlmzptqA5evry4MH\nD9i1axdZWVnMnTuXBw8eFGm9gwYNYubMmWoMOXnypNqoVKFCBVxdXcnMzGTOnDkGw27Xrl0brVar\nxk83Nzc6duzI5MmTuX37Njk5OSQlJeV7V6+xvI5LaWlp3Lhxw+CJVw8PD9q0acO0adN48OABJ0+e\nZPXq1VZvxou6XL169WjRogWzZs0iKyuLQ4cOsWPHDmu70KqmTZuya9cubty4oV4LLLHUGbQoHSBt\nVXmNs23atMHe3p7PPvuMrKwstm7dytGjR1EUhSFDhrBixQqOHDmCTqcjIyODnTt3qo3E+nGkoIwr\n9J5++mnq1KnD+vXryc7OZtWqVQbTY2Nj1cbuatWqoSjKY7vfshaXCqOoMSuP8X7p2rWrxTKqpcpN\nSxWqQpRm5TXOGrOV8mx5zKe5+mSdTleoetoGDRoQHBysriM9Pd2gPvjSpUu88cYbeHl5ERgYyPPP\nP6/WdRrXxxbGpEmT1PeXz5492+Cpq02bNqHVamnSpAkajQaNRsOCBQvYvn07N2/epHv37ur3+vWu\nPXr0IDU1lVdffbVA77MsbJuEvldeeYXExERq165N48aN1e9fffVVxo0bx/Dhw/H09OT5559X323+\nJFQo7AI1nCsQ5FmtONKirr8gGjduzO7du/N9b/wCeci9yRsyZIjVeTUajcHj8s888wxbt241uf1l\ny5YZfG7Xrp3BO0natm1r8Yf09vZm6tSp+b43TgPAli1b1P+dnJyYNWuWyZfdGqcBcg/K0aNHM3r0\naLNpMaVt27bs2rUr3/e9evUyeJ+VtbS7urri7Oyc7+azYsWKzJw5k5kzZ+Zbz6uvvqoOp2dKQECA\n+rJbfabyr/84d7Vq1czefA4YMIABAwYYfPfMM8/k+w0jIiJMrhtMH3sFSaOl3xTM7w/j40J/DPRW\nrVqZPXZbt27NL7/8YvCduacJrB3nxr93jRo1mDdvnsl1WVKvXj3c3d3zPcVh6RysUaMGGzduNPhO\n/zcwTpt+Xuzt7ZkxYwYzZswAcp90k961oiwqLdfkjz76iNGjR/Pvf//bZMyy1mkgb/rChQuJiIhg\n0KBB6nA71jqhTJo0ibfffhsfHx+DTijOzs40b96cSpUqqZ0rWrduzf/+9z+LQxcXpJODr68vEyZM\noE+fPlSqVImpU6cavKDdUrqrVq3Kxo0bmTRpEnPmzMHJyYlRo0bRqlUrg23379+fPXv20KRJE2rU\nqMG7777LypUr1fWtX7+e6OhosrOz8fPzUzsiJCYmEh0dzdWrV6lWrRrDhg0z+YScpc5B9vb2rF27\nlqlTp9KyZUsePHiAn5+fOixDVFQUb7/9Ni1atKBOnToMGDCAzz//3OI+E6KsKy3xFmD8+PFER0fz\n/vvv8/bbb/Pxxx8zbtw4srOzGTNmjMFQaJbOdWMRERFkZmYSEhLC1atXadCgAV999RWdO3emU6dO\ntG7dmsqVKxMeHm4wTFjv3r1Zv349Pj4+eHl5sWfPHj755BM+/PBDAgMDuXPnDl5eXibL7/qGDBnC\nuXPnaN++PU899RQREREGQ/n861//4u2336Zx48a4uLgwceJE2rdvbzKf+v8/ynKjR4/Gx8eHZ599\nlgEDBnDz5k2LebC0fyE3tv/44480b94cT09PBgwYwCeffGJ2/rzOoGfOnMHBwYHnnnuOBQsWALkd\nIHU6HSEhIVy8eJGaNWsSHBzMK6+8YjWN4tGVhpiQ10HyrbfeYsaMGXTt2pWePXsCuZXUCxcuJDo6\nmoSEBCpVqkTbtm3VzkH6ceSdd94x2wlU/7uRI0cSERHB//3f/9G/f38++ugjFi5cyIQJE5g2bRqD\nBg0yeI/YsWPH1MammjVrqh1si0r/fLUWlwoS+/Q/W4pZ1tZlar9YKqNOmjSJ4cOHU79+fYKCgujb\nty83btwAcitU3377bdLS0qhcuTJ9+vQp0PBWQghRmpWGayaYrk8uSj2t8Yhi+nWRISEhBo1W+kzV\nx5pjXOfs6elpsp4eDOuHjVmqq65UqRI1a9Ys8HWmMG0SxumvWLGiwTCM+vr161fgJ88eN8VSz+gj\nR478UL9+/Q5PMD2PlVarJSAggCtXrpSqJ0F69epFaGgogwcPLumkFLtly5axe/duqy/bLqgWLVqw\nePFi9WZWlB4RERHUrVs333i21mRlZTFs2DD8/f0NehoUp/v377Nv3z46duzI5cuXef3112nTpo3a\nUKYvLS3N5HA7cXFxdO7cuVyPvzhv3jxd3vBs5dn+/fvLRI8oc8eiEGWdLcdZkFhbGkm8FeWNxNlH\ni7MSE4QQBWHLsVbKs+XLo+SztF4zpT45v61bt/Lhhx+q7ysvCx53nC30E2NlTWl9b1BJp2vDhg0G\nQxDmqVevXoHH0ramefPmKIpSoHc/lSfz589n4cKF+b4PDAzk66+/LoEUPTmmjuuDBw+a7X2we/du\nOnXqRLNmzQgPDy/u5Kl0Oh2zZ89m2LBhVKpUiZdeeqnQjzELIYQQQgghhBBCCCFEYViqKzX3ZNXj\n1LNnT86ePcunn35q8H1oaCg///xzvvkjIyN56623ij1dT1q5bhgzNbRfaWDpEccnJTQ0NN97vx63\n33777bGv0/hRzNIoMjKSyMjIkk7GE2c87GKewMBAi0Hd+J1pT0KlSpVMDsUqDMk44cKW2XInB/Fk\nSawVtiQyMpKYmJh83/fr14+5c+eWQIoK70l0MBSPl63E2bKgpCsChRDFw1birK2UZ8tjPktbfbK1\nutLiZu61NRs2bHjCKSlZ5bphTAghhBCiKGy1k4MQQhSn+fPnM3/+/JJOxiN5Eh0MhSivSroiUAgh\nhBAiT+l58ZYQQohSpbT1qCku+/fvL+kkCCFsmMRaIYQoXrYSZ4UQoqTYSpy1lfKsreRTCGkYE0II\nIYQQQgghhBBCCCGEEDZBGsaEEEKYJOOECyFE8ZNYK4QQxctW4qwQQpQUW4mztlKetZV8CiENY0II\nIYQQQgghhBBCCCGEEMImSMOYEEIIk2SccFFQzZs358cffyw163nctFotrq6u5OTklHRSCmzWrFmE\nh4eXdDJEAUisFU/a22+/zdy5c9XP//d//0fDhg3RaDTcuHGjBFMmRPGwlThbUK6urpw/f97ktA0b\nNhASElIs2y0N5byIiAhmzJhRomkor/bv30/Tpk3NTje+9lhSmHkftwULFjBu3LgS2XZZZitx1lbK\ns7aSTyEqFHaB+5eu8uDy1eJICwBOtVypWNu12NZvTlBQEHPnziUoKOiJb9sarVZLQEAAV65cwc7O\nclvmrFmzOH/+PMuXLyc1NZWgoCCSk5NRFOWxLac/rxBCiJJTWq7JiqJYvc4UxONaj6DE9+P+/fvp\n3bs3//znPxk7dmyJpkWIx6G0xNvCWrNmDatWrWLbtm3qd/PmzVP/z8rKYurUqezatYvGjRs/0rZc\nXV05cuQIXl5ej7QeIcqCshoTrAkNDSU0NLRY1l1aynmlIQ0lpSTrcvSvPY9z3sdt/PjxJbZtIcqr\n0nrNjIiIoG7dukyePPmR01CYOvjHxVLZ29Q9gDk9e/akX79+DB48uBhSWboVumHsweWrXP52b3Gk\nBYBa3doX+GBu3rw5S5YsoX379o+83QMHDjzyOkpDg5H+yefh4YFWq33sy9lyQVIIWyLjhJd+pema\nXNyys7Oxt7cv6WSUGTqdrkS3v3btWho1asS6deukYcwKibVlQ1mMtw8fPrQ6z6VLl7h//z4NGzZ8\nLNss6dgjhCnFEWfLYkwoqx4+fEiFCoWuujKrKHEqbxlbrwt53L9FaSP3G0Un5dny5XHnszRfMwsS\n1wvS/mBcl16WGptKS8eVklCmh1JUFEVuvowUdX/IfhRCCPEojh8/zgsvvICXlxfDhg3jwYMHBAUF\nsWPHDnWerKwsfH19OXHiBABff/01/v7++Pr6Mn/+fIP1zZo1i9dff53w8HA8PT1Zu3YtFy9eZODA\ngfj4+PDss8/y5ZdfGsw/dOhQwsPD0Wg0tGvXjoSEBBYsWEDDhg3x9/fn+++/V+e/desWY8aMoXHj\nxjRp0oQZM2aoQyXm5OQwdepU/Pz8aNmyJTt37jRIm/FQQPrDFt6/f5+RI0fi6+uLt7c3Xbp04cqV\nKwCsXr2atm3botFoaNmyJStXrjRY744dO2jfvj3e3t5069aN+Ph4q/t90aJFNGnSBI1Gw3PPPcfe\nvbk3HIqikJmZyejRo9FoNAQFBRkMcbJw4UJatWqFRqMhMDCQ//73v+q0NWvW0K1bN6Kjo/Hy8jJY\nr7V9B5CRkcHWrVuZN28eqampNjO0ihBPirnzN+/cnTx5Mr6+vgwbNox33nmHX3/9FY1GQ/369YG/\nhhJLSEigbdu2AHh7e9OnTx8AJk6cSLNmzfD09KRTp04cOnRI3XZOTg7z589Xt9+5c2cuXLjAq6++\nCkD79u3RaDTExsZy9epVwsLC8Pb2xsfHh1dffVXuOYR4zC5evMiQIUNo0KABAQEBfP7550BuBb/+\nudqpUyfS0tLU5X744Qdat26Nt7c3UVFR6vdr1qyhe/fu6mdXV1dWrlxpcl6dTsfcuXNp3rw5DRs2\nZPTo0dy6dUudbqmcp9Pp1Fjm6+vL3//+d3Uo17whrFetWoW/vz+vvfYaa9euVeObt7c3rVq14uef\nf2b16tU0a9aMhg0bsm7dukLtuxs3bhAWFkaDBg2oX78+AwYMMNhHPXv2ZMaMGXTr1g0PDw+WLFlC\n586dDdbxySef8Le//Q2AnTt30qFDBzw9PWnWrBmzZ8+2mgZL5UZL5a2kpCR69+6Nr68vfn5+jBw5\n0mDfmyof7t69m4ULF7J582Y0Gg0dOnQALJdP9+/fT5MmTVi8eDGNGjUqUGenBQsW4OfnR4sWLYiJ\niVG/1x/GMm+9hZl32bJlNGzYkMaNG7NmzRqDeSdMmEBYWBgajYauXbsaDBVq6ZpmfL+xZs2afMOR\nDx06lEaNGuHl5UWPHj04ffq01X0ghCg7ClI2tdb+YKozmq02NJU1ZbZhLDw8nNTUVAYOHIhGo2Hx\n4sX5xjNu3ry5WpEza9Ys3njjDbMVRPqVXJYKkeYuquYKGdYqj0yxViFnqWJQn/E7UYq6XHJyMj16\n9ECj0RAcHMy1a9cspl8IUT7YSmW2jJ/96HQ6Hf/5z3+IiYnh2LFjnDx5krVr1xIWFsb69evV+Xbt\n2kWdOnVo2rQpp0+fZsKECXz++efEx8dz7do1g8oIgG+//ZbevXuTnJxM3759GT58OB4eHpw6dYqV\nK1cyffp09u3bp86/c+dO+vfvT1JSEv7+/gQHBwMQHx/PO++8Q2RkpDpvREQEjo6OHDlyhB9//JHv\nv/9evS5+8cUX7Ny5kx9//JE9e/awZcsWg4KtcY8q/c/r1q3j9u3bnDhxgsTERObPn0/FihUBqFWr\nFl9//TVarZalS5cyZcoUjh8/DuQ2LI4dO5aFCxeSmJjI0KFDGThwIJmZmWb3+9mzZ/n3v//Nnj17\n0Gq1bNy4EY1Go/4m3377LcHBwSQnJ/PKK68YVGZ5e3uzbds2tFotUVFRhIeHc/nyZXV6XFwc3t7e\nJCQkMHHiRIYMGcLNmzet7juAb775hlq1avHcc8/RrVu3QldU2RqJtaKwTJ2/ly5dAv46d8+cOcNn\nn33GvHnzaN26NVqtlsTERHUdiqLg4+PDwYMHATh//jybN28GoFWrVuzbt4+kpCRCQkJ444031Fi0\ndOlSNm3axPr169FqtSxevBhnZ2e1cW7fvn1otVpee+01li1bhru7O+fOnePMmTNMnTpVKglEiSiv\ncTYnJ4eBAwfi7+9PfHw8sbGxLF++nD179rBs2TKDc3XJkiVUqlRJXXbnzp1899137Nu3j9jYWL77\n7juz2zE37+rVq1m3bh1bt24lLi6OO3fuEB0dDWC1nPfZZ5+xfft2vvnmG06dOoWLiwsTJkww2O7B\ngwf5+eefiYmJQafTERcXR9OmTUlMTCQ4OJi///3vHD9+nLi4OJYvX05UVBR3794t8P7T6XQMGjSI\n48ePc/z4cSpWrKimP8/69etZtGgRKSkpDB06lLNnzxrE0o0bN9K3b18AKleuzPLly0lOTubrr79m\nxYoVVoewslRutFbeioyM5NSpUxw6dIgLFy4wa9YswHz5sEuXLowfP57g4GC0Wq1a/2WpfApw5coV\nbty4wfHjx/M1cBq7fPky165dIz4+nk8++YTx48eTkJCgTte/Bly5cqVQ896+fZv4+HgWLVpEVFSU\nQUPg5s2biY6OJikpifr16zN9+nR1mqVrGhjeb4SGhua7Tr300kscPnyYs2fP4u/vz8iRIy3uA1tV\nXuOsMVspz5bXfB4/fpwXX3wRjUajdubNY66TqnH7w5IlS/J14OjTpw8pKSm4urqSnZ3N9OnTOXjw\nINHR0Wg0GiZOnGjyveU9e/bkq6++AiAxMZEePXrg5eWFn58fw4YNK1Cedu7cScuWLfHz8+Of//yn\n2Qa8n3/+mc6dO+Pl5UWXLl345ZdfDKYnJSXRpUsXPD09GTRokM28d7jMNowtX74cDw8P1q5di1ar\npWXLlvnmMb6g7dixw2wFkX6llnEhcunSpWoh0txF1Vwhw1phxhRrFXLWKgbNKepy//jHPwgICCAh\nIYEJEyawdu1auakVQgihUhSFkSNHUrt2bVxcXOjWrRu///47/fr1Y9euXdy5cwfI7Tncr18/ALZs\n2cLLL79M27ZtcXR0ZNKkSfneo9mmTRteeeUVAP744w9++eUX/vnPf+Lo6EjTpk0ZPHiwQaNLYGAg\nHTt2xN7enl69enH9+nXeeust7O3t6dOnD1qtllu3bnH58mV2797NjBkzqFSpEk8//TSjRo1SK4Vj\nY2MZNWoUdevWxcXFhfHjx1vsIabT6dTpDg4OXLt2jcTERBRFwd/fn6pVqwLQtWtXPD09gdx3m3bs\n2FGtlP7iiy94/fXXadmyJYqiEBYWhpOTE4cPHza7XXt7ezIzMzl9+jRZWVl4eHgYjC/etm1bunTp\ngqIohIaGcvLkSXVa7969qV27NgB9+vShfv36HDlyRJ1es2ZNwsPD1X3n6+vLjh07rO47yK3k6d27\nt7qdTZs2FWhINyFEwZg6f+Pi4gBwc3Nj+PDh2NnZUbFiRbOxK+97U9NDQ0NxcXHBzs6OiIgIHjx4\nwLlz5wBYtWoVU6ZMwcfHB4AmTZpQvXp1k9twcHDg0qVLaLVa7O3t1afThBCPR1xcHFevXuWdd96h\nQoUKeHp6MnjwYDZu3Mjq1astnqvjxo3jqaeewsPDg3bt2qlP85tiPG9eeSImJoaIiAg0Gg2VK1fm\nvffeY9OmTWRnZ1st561cuZLJkydTp04dHBwciIqKYsuWLQYVhtHR0VSqVEltKPL09GTAgAEoikKf\nPn1IT09nwoQJODg40LFjRxwdHUlKSirw/qtevTo9evSgYsWKVKlShcjISH766Sd1uqIoDBgwgIYN\nG2JnZ8dTTz1F9+7d2bhxIwAJCQmcPXtWLas+//zzNGrUCIDGjRvTp08fg/WZYq7caK285e3tTYcO\nHXBwcMDV1ZVRo0aprwexVD7UL7PmsVQ+BbCzs2PixIk4ODiov4UlkyZNwsHBgaCgILp27WpQRjTe\ndkHnzTtG7O3t6dq1K5UrV+bs2bPq9B49ehAQEIC9vT19+/bl999/V6dZuqaB4f2GqevmwIEDqVy5\nMg4ODkRHR3PixAlu375tdT8IIUqXzMxMBg0aRFhYmPrU7datW1EUxWwn1aysrHztD2PGjFHXadyB\nA3KvHVOmTCEwMJA5c+ag1WrVjgvG9NsiZs6cSefOnTl//jwnT55kxIgRBcrXtm3b+P777/n+++/Z\nvn07q1atyjfP9evXCQsLIzw8nMTEREaNGkVYWJja+KXT6Vi3bh1Lly7l1KlT2NvbM3HixELt37Kq\nzDaMFYWlCiJ9xjd8jRs3VguRli6qxoWMglQemWKpQi41NdVqxaApj7LcsWPH1AJLYGAg3bp1k2FQ\nhLABMk64KIxatWqp/1esWJG7d+/i5uZGmzZt2LJlCzdv3mTPnj3qC90vXbpE3bp11WWcnZ2pUaOG\nwTr1p6enp1O9enUqV66sfufh4cHFixfVzzVr1jRIQ40aNdSCZl4Hl4yMDFJSUsjKyqJRo0Z4e3vj\n7e1NZGQkf/zxh7otd3d3g+1Yot9ZpH///nTq1Ilhw4bRpEkT3n//fbVRaNeuXXTt2hUfHx+8vb3Z\ntWuX+hR2SkoKn3zyiZoeb29v0tLSSE9PN7vd+vXrM3PmTGbPnk3Dhg0ZPny4wfz6v4mzszP3799X\nK5zWrVtHhw4d1G2dOnXK4InwOnXqGGyrXr16pKenk5qaanHfpaamsn//frVhrHPnzty/fz/f0+/i\nLxJrRWGZOn+vXr2KoigGsauolixZQtu2bfHy8sLb25tbt25x9WruC9PT0tJMvuDblDFjxuDt7U1I\nSAgtW7Zk0aJFj5w2IYqivMbZlJQU0tPTDcoOCxYs4I8//uDChQsWz9W8xnXILSNlZGQUeN68Dk/p\n6ekGZSQPDw8ePnzI5cuXrZbzUlJSGDx4sJruwMBAKlSoYPD0unE8My7nATz99NMG3+WlrSDu3r3L\n+PHjad68OZ6envTo0YNbt24Z1HUYpyEkJERtGIuJiVEb1gAOHz5Mr169aNCgAV5eXnzxxRdcv37d\nYhrMlRutlVUvX76sLuPp6cmoUaPUcpy18qExS+VTyB1O09HRsUD71MXFxeDJxHr16qlPND/KvNWr\nVzdoWDU+ZvWPDeNplq5pYHi/YSw7O5sPPviAVq1a4enpSYsWLVAURUZRMqG8xlljtlKeLY/5PHz4\nMNnZ2Wrnz169ehEQEIBOp+PLL78sdCdV+KsDh5OTk8nphak7d3R0RKvVkpaWhqOjI88991yBlhs7\ndizVqlXDw8OD8PBwNm3alG+enTt34uvrS2hoKHZ2doSEhODn58f27dsB1Dw/88wzODs7M2nSJGJj\nY22i7t+mGsYsVRDps3TDZ+2iqs9aYcYcSxVyBakYNLfOoix38eJFkwUWIYQQwpK8QtSAAQPYsGED\nsbGxtG7dGjc3NyC3kuXChQvqVNM92wAAIABJREFU/Hfv3s13k6nf4OTm5sb169cNKjxSU1Mt3sya\n4+7ujpOTEwkJCSQlJZGUlERycrLaq9fNzc0gbampqQbLOzs7GwzVo38TX6FCBaKiojh48CDffvst\nO3bsYN26dTx48IChQ4cyduxYzpw5Q1JSEl27dlX3k4eHB5GRkWp6kpKSSEl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L8CLTPeagH8DGzP+H/me9muQlBK+QOrgHlAaYzB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ITpSzylkRyZ6yVlkrItlS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\naGJMREREREREREREREREgvD/pd8H7mkmfSAAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x118b6dd90>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the (truncated) figure above, you will see some cases where the classes are well separated and others were they are not. It is typical that one single feature will not allow you to completely separate more than thirty distinct classes. You will need to be creative in coming up with additional metrics to discriminate between all the classes."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Random Forest Classification\n",
      "\n",
      "We choose a random forest model to classify the images. Random forests perform well in many classification tasks and have robust default settings. We will give a brief description of a random forest model so that you can understand its two main free parameters: n_estimators and max_features.\n",
      "\n",
      "A random forest model is an ensemble model of n_estimators number of decision trees. During the training process, each decision tree is grown automatically by making a series of conditional splits on the data. At each split in the decision tree, a random sample of max_features number of features is chosen and used to make a conditional decision on which of the two nodes that the data will be grouped in. The best condition for the split is determined by the split that maximizes the class purity of the nodes directly below. The tree continues to grow by making additional splits until the leaves are pure or the leaves have less than the minimum number of samples for a split (in sklearn default for min_samples_split is two data points). The final majority class purity of the terminal nodes of the decision tree are used for making predictions on what class a new data point will belong. Then, the aggregate vote across the forest determines the class prediction for new samples.\n",
      "\n",
      "With our training data consisting of the feature vector X and the class label vector y, we will now calculate some class metrics for the performance of our model, by class and overall. First, we train the random forest on all the available data and let it perform the 5-fold cross validation. Then we perform the cross validation using the KFold method, which splits the data into train and test sets, and a classification report. The classification report provides a useful list of performance metrics for your classifier vs. the internal metrics of the random forest module."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print \"Training\"\n",
      "# n_estimators is the number of decision trees\n",
      "# max_features also known as m_try is set to the default value of the square root of the number of features\n",
      "clf = RF(n_estimators=100, n_jobs=3);\n",
      "scores = cross_validation.cross_val_score(clf, X, y, cv=5, n_jobs=1);\n",
      "print \"Accuracy of all classes\"\n",
      "print np.mean(scores)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Training\n",
        "Accuracy of all classes"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "0.466882620064\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kf = KFold(y, n_folds=5)\n",
      "y_pred = y * 0\n",
      "for train, test in kf:\n",
      "    X_train, X_test, y_train, y_test = X[train,:], X[test,:], y[train], y[test]\n",
      "    clf = RF(n_estimators=100, n_jobs=3)\n",
      "    clf.fit(X_train, y_train)\n",
      "    y_pred[test] = clf.predict(X_test)\n",
      "print classification_report(y, y_pred, target_names=namesClasses)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                                                                      precision    recall  f1-score   support\n",
        "\n",
        "                 competition_data/train/appendicularian_slight_curve       0.36      0.47      0.41       532\n",
        "                          competition_data/train/hydromedusae_shapeB       0.41      0.11      0.17       150\n",
        "                          competition_data/train/hydromedusae_shapeA       0.41      0.77      0.54       412\n",
        "                     competition_data/train/siphonophore_other_parts       0.00      0.00      0.00        29\n",
        "                      competition_data/train/tunicate_doliolid_nurse       0.19      0.05      0.08       417\n",
        "                           competition_data/train/acantharia_protist       0.41      0.85      0.55       889\n",
        "                     competition_data/train/hydromedusae_narco_young       0.28      0.07      0.12       336\n",
        "                        competition_data/train/fish_larvae_deep_body       0.00      0.00      0.00        10\n",
        "        competition_data/train/hydromedusae_haliscera_small_sideview       0.00      0.00      0.00         9\n",
        "               competition_data/train/echinoderm_larva_pluteus_early       0.53      0.28      0.37        92\n",
        "   competition_data/train/siphonophore_calycophoran_rocketship_young       0.41      0.29      0.34       483\n",
        "                          competition_data/train/chaetognath_sagitta       0.42      0.20      0.27       694\n",
        "                            competition_data/train/shrimp-like_other       0.00      0.00      0.00        52\n",
        "                         competition_data/train/hydromedusae_liriope       0.00      0.00      0.00        19\n",
        "                                    competition_data/train/heteropod       0.00      0.00      0.00        10\n",
        "                     competition_data/train/appendicularian_straight       0.25      0.01      0.02       242\n",
        "                      competition_data/train/chaetognath_non_sagitta       0.56      0.73      0.64       815\n",
        "                           competition_data/train/trochophore_larvae       0.00      0.00      0.00        29\n",
        "               competition_data/train/copepod_cyclopoid_oithona_eggs       0.53      0.79      0.63      1189\n",
        "                            competition_data/train/ctenophore_cestid       0.00      0.00      0.00       113\n",
        "                           competition_data/train/pteropod_butterfly       0.45      0.05      0.08       108\n",
        "                    competition_data/train/hydromedusae_sideview_big       0.50      0.01      0.03        76\n",
        "   competition_data/train/siphonophore_calycophoran_rocketship_adult       0.42      0.06      0.10       135\n",
        "                              competition_data/train/shrimp_caridean       0.63      0.24      0.35        49\n",
        "                           competition_data/train/hydromedusae_typeD       0.00      0.00      0.00        43\n",
        "                      competition_data/train/appendicularian_s_shape       0.37      0.54      0.44       696\n",
        "                             competition_data/train/crustacean_other       0.26      0.12      0.16       201\n",
        "                       competition_data/train/fish_larvae_myctophids       0.54      0.51      0.52       114\n",
        "                    competition_data/train/hydromedusae_partial_dark       0.65      0.25      0.36       190\n",
        "                        competition_data/train/copepod_calanoid_eggs       0.73      0.17      0.28       173\n",
        "                                   competition_data/train/stomatopod       0.00      0.00      0.00        24\n",
        "                 competition_data/train/siphonophore_physonect_young       0.00      0.00      0.00        21\n",
        "                     competition_data/train/hydromedusae_solmundella       0.75      0.17      0.28       123\n",
        "                                       competition_data/train/ephyra       1.00      0.07      0.13        14\n",
        "              competition_data/train/hydromedusae_bell_and_tentacles       0.20      0.01      0.03        75\n",
        "                            competition_data/train/pteropod_triangle       1.00      0.03      0.06        65\n",
        "                             competition_data/train/hydromedusae_h15       0.60      0.17      0.27        35\n",
        "                          competition_data/train/diatom_chain_string       0.54      0.92      0.68       519\n",
        "                    competition_data/train/hydromedusae_narcomedusae       0.00      0.00      0.00       132\n",
        "                       competition_data/train/copepod_calanoid_large       0.51      0.44      0.47       286\n",
        "                           competition_data/train/radiolarian_colony       0.51      0.26      0.34       158\n",
        "                             competition_data/train/tunicate_partial       0.61      0.95      0.75       352\n",
        "                  competition_data/train/invertebrate_larvae_other_B       0.00      0.00      0.00        24\n",
        "                  competition_data/train/invertebrate_larvae_other_A       0.00      0.00      0.00        14\n",
        "         competition_data/train/echinoderm_larva_pluteus_brittlestar       0.50      0.03      0.05        36\n",
        "           competition_data/train/siphonophore_calycophoran_abylidae       0.26      0.05      0.09       212\n",
        "                            competition_data/train/euphausiids_young       0.00      0.00      0.00        38\n",
        "                         competition_data/train/hydromedusae_aglaura       0.00      0.00      0.00       127\n",
        "                          competition_data/train/protist_dark_center       0.00      0.00      0.00       108\n",
        "                         competition_data/train/trichodesmium_bowtie       0.42      0.66      0.51       708\n",
        "                            competition_data/train/radiolarian_chain       0.44      0.04      0.08       287\n",
        "                          competition_data/train/protist_fuzzy_olive       0.72      0.78      0.75       372\n",
        "                                   competition_data/train/polychaete       0.20      0.01      0.01       131\n",
        "                             competition_data/train/copepod_calanoid       0.42      0.58      0.48       681\n",
        "                                    competition_data/train/amphipods       0.50      0.04      0.08        49\n",
        "                competition_data/train/acantharia_protist_big_center       0.00      0.00      0.00        13\n",
        "                    competition_data/train/copepod_calanoid_octomoms       0.00      0.00      0.00        49\n",
        "                                competition_data/train/protist_other       0.36      0.69      0.48      1172\n",
        "                           competition_data/train/hydromedusae_other       0.00      0.00      0.00        12\n",
        "                                competition_data/train/tunicate_salp       0.57      0.75      0.65       236\n",
        " competition_data/train/siphonophore_calycophoran_sphaeronectes_stem       0.00      0.00      0.00        57\n",
        "                           competition_data/train/trichodesmium_puff       0.71      0.93      0.81      1979\n",
        "                                    competition_data/train/artifacts       0.53      0.83      0.65       393\n",
        "                     competition_data/train/fish_larvae_leptocephali       0.00      0.00      0.00        31\n",
        "          competition_data/train/echinoderm_larva_seastar_bipinnaria       0.54      0.62      0.58       385\n",
        "                               competition_data/train/chordate_type1       0.51      0.55      0.53        77\n",
        "                                  competition_data/train/shrimp_zoea       0.54      0.21      0.31       174\n",
        "                   competition_data/train/fish_larvae_very_thin_body       0.00      0.00      0.00        16\n",
        "             competition_data/train/ctenophore_cydippid_no_tentacles       0.00      0.00      0.00        42\n",
        "                competition_data/train/appendicularian_fritillaridae       0.00      0.00      0.00        16\n",
        "                       competition_data/train/siphonophore_physonect       0.00      0.00      0.00       128\n",
        "                           competition_data/train/trichodesmium_tuft       0.37      0.46      0.41       678\n",
        "                                 competition_data/train/fecal_pellet       0.31      0.28      0.29       511\n",
        "           competition_data/train/hydromedusae_shapeA_sideview_small       0.34      0.09      0.14       274\n",
        "      competition_data/train/siphonophore_calycophoran_sphaeronectes       0.47      0.12      0.19       179\n",
        "                               competition_data/train/artifacts_edge       0.92      0.79      0.85       170\n",
        "                competition_data/train/ctenophore_cydippid_tentacles       0.00      0.00      0.00        53\n",
        "                    competition_data/train/copepod_cyclopoid_oithona       0.48      0.60      0.54       899\n",
        "                         competition_data/train/siphonophore_partial       0.00      0.00      0.00        30\n",
        "                            competition_data/train/tunicate_doliolid       0.29      0.21      0.25       439\n",
        "                                competition_data/train/copepod_other       0.00      0.00      0.00        24\n",
        "                    competition_data/train/unknown_blobs_and_smudges       0.25      0.13      0.17       317\n",
        "                           competition_data/train/shrimp_sergestidae       0.50      0.05      0.09       153\n",
        "                        competition_data/train/hydromedusae_solmaris       0.41      0.52      0.46       703\n",
        "                   competition_data/train/copepod_calanoid_flatheads       0.71      0.03      0.05       178\n",
        "        competition_data/train/echinoderm_larva_seastar_brachiolaria       0.67      0.77      0.71       536\n",
        "                   competition_data/train/copepod_calanoid_eucalanus       0.91      0.10      0.19        96\n",
        "                            competition_data/train/ctenophore_lobate       0.84      0.55      0.67        38\n",
        "                         competition_data/train/detritus_filamentous       0.18      0.02      0.03       394\n",
        "                            competition_data/train/jellies_tentacles       0.36      0.04      0.06       141\n",
        "                                competition_data/train/detritus_blob       0.21      0.06      0.09       363\n",
        "                            competition_data/train/chaetognath_other       0.42      0.75      0.54      1934\n",
        "                    competition_data/train/copepod_cyclopoid_copilia       0.00      0.00      0.00        30\n",
        "    competition_data/train/copepod_calanoid_large_side_antennatucked       0.48      0.21      0.29       106\n",
        "                       competition_data/train/trichodesmium_multiple       0.57      0.07      0.13        54\n",
        "                        competition_data/train/fish_larvae_thin_body       0.29      0.03      0.06        64\n",
        "                            competition_data/train/diatom_chain_tube       0.40      0.41      0.40       500\n",
        "                         competition_data/train/tunicate_salp_chains       0.33      0.01      0.03        73\n",
        "                                 competition_data/train/protist_star       0.89      0.50      0.64       113\n",
        "                      competition_data/train/fish_larvae_medium_body       0.63      0.28      0.39        85\n",
        "                      competition_data/train/hydromedusae_narco_dark       0.00      0.00      0.00        23\n",
        "                       competition_data/train/hydromedusae_haliscera       0.55      0.39      0.46       229\n",
        "                           competition_data/train/hydromedusae_typeE       0.00      0.00      0.00        14\n",
        "                           competition_data/train/hydromedusae_typeF       0.48      0.20      0.28        61\n",
        "              competition_data/train/echinoderm_larva_pluteus_urchin       0.67      0.40      0.50        88\n",
        "competition_data/train/siphonophore_calycophoran_sphaeronectes_young       0.50      0.06      0.10       247\n",
        "                            competition_data/train/protist_noctiluca       0.60      0.52      0.56       625\n",
        "              competition_data/train/copepod_calanoid_frillyAntennae       1.00      0.02      0.03        63\n",
        "     competition_data/train/echinoderm_seacucumber_auricularia_larva       1.00      0.01      0.02        96\n",
        "                   competition_data/train/tornaria_acorn_worm_larvae       0.85      0.45      0.59        38\n",
        "                               competition_data/train/detritus_other       0.26      0.33      0.29       914\n",
        "                               competition_data/train/unknown_sticks       0.33      0.02      0.03       175\n",
        "                         competition_data/train/unknown_unclassified       0.00      0.00      0.00       425\n",
        "                       competition_data/train/pteropod_theco_dev_seq       0.00      0.00      0.00        13\n",
        "                      competition_data/train/acantharia_protist_halo       1.00      0.04      0.08        71\n",
        "        competition_data/train/hydromedusae_typeD_bell_and_tentacles       1.00      0.05      0.10        56\n",
        "               competition_data/train/echinoderm_larva_pluteus_typeC       0.81      0.16      0.27        80\n",
        "                                     competition_data/train/decapods       0.00      0.00      0.00        55\n",
        "          competition_data/train/copepod_calanoid_small_longantennae       0.85      0.13      0.22        87\n",
        "                                  competition_data/train/euphausiids       0.78      0.10      0.18       136\n",
        "                                competition_data/train/echinopluteus       0.00      0.00      0.00        27\n",
        "\n",
        "                                                         avg / total       0.44      0.47      0.41     30336\n",
        "\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The current model, while somewhat accurate overall, doesn't do well for all classes, including the shrimp caridean, stomatopod, or hydromedusae tentacles classes. For others it does quite well, getting many of the correct classifications for trichodesmium_puff and copepod_oithona_eggs classes. The metrics shown above for measuring model performance include precision, recall, and f1-score. The precision metric gives probability that a chosen class is correct, (true positives / (true positive + false positives)), while recall measures the ability of the model correctly classify examples of a given class, (true positives / (false negatives + true positives)). The F1 score is the geometric average of the precision and recall.\n",
      "\n",
      "The competition scoring uses a multiclass log-loss metric to compute your overall score. In the next steps, we define the multiclass log-loss function and compute your estimated score on the training dataset."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def multiclass_log_loss(y_true, y_pred, eps=1e-15):\n",
      "    \"\"\"Multi class version of Logarithmic Loss metric.\n",
      "    https://www.kaggle.com/wiki/MultiClassLogLoss\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    y_true : array, shape = [n_samples]\n",
      "            true class, intergers in [0, n_classes - 1)\n",
      "    y_pred : array, shape = [n_samples, n_classes]\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    loss : float\n",
      "    \"\"\"\n",
      "    predictions = np.clip(y_pred, eps, 1 - eps)\n",
      "\n",
      "    # normalize row sums to 1\n",
      "    predictions /= predictions.sum(axis=1)[:, np.newaxis]\n",
      "\n",
      "    actual = np.zeros(y_pred.shape)\n",
      "    n_samples = actual.shape[0]\n",
      "    actual[np.arange(n_samples), y_true.astype(int)] = 1\n",
      "    vectsum = np.sum(actual * np.log(predictions))\n",
      "    loss = -1.0 / n_samples * vectsum\n",
      "    return loss"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Get the probability predictions for computing the log-loss function\n",
      "kf = KFold(y, n_folds=5)\n",
      "# prediction probabilities number of samples, by number of classes\n",
      "y_pred = np.zeros((len(y),len(set(y))))\n",
      "for train, test in kf:\n",
      "    X_train, X_test, y_train, y_test = X[train,:], X[test,:], y[train], y[test]\n",
      "    clf = RF(n_estimators=100, n_jobs=3)\n",
      "    clf.fit(X_train, y_train)\n",
      "    y_pred[test] = clf.predict_proba(X_test)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "multiclass_log_loss(y, y_pred)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "3.7485740280854944"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The multiclass log loss function is an classification error metric that heavily penalizes you for being both confident (either predicting very high or very low class probability) and wrong. Throughout the competition you will want to check that your model improvements are driving this loss metric lower."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Where to Go From Here\n",
      "\n",
      "Now that you've made a simple metric, created a model, and examined the model's performance on the training data, the next step is to make improvements to your model to make it more competitive. The random forest model we created does not perform evenly across all classes and in some cases fails completely. By creating new features and looking at some of your distributions for the problem classes directly, you can identify features that specifically help separate those classes from the others. You can add new metrics by considering other image properties, stratified sampling, transformations, or other models for the classification."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}